Natural Language Understanding (NLU) systems have been built for specific tasks covering one or more domains. The Air Travel Information System (ATIS) was a large scale effort to build NLU systems covering the air travel and the hotel reservations domains. Later, such systems were built by various groups covering specific tasks. Many of these systems are built in a fully supervised fashion.
An NLU system can be designed for handling only the air travel reservation task in the air travel domain, but a user may expect the system to handle actions the system is not designed to handle, such as checking the flight status. Checking the flight status is related to the original air travel reservation task but not precisely within the air travel reservation domain. The standard approach to solve this problem is to manually redesign the semantic schema for the air travel reservation domain and add new intents and slots to cover the check-flight status function. This requires collecting and annotating additional data and retraining the NLU models, which is time consuming and expensive.